An improved cognitive model of the Iowa and Soochow Gambling Tasks with regard to model fitting performance and tests of parameter consistency.

Junyi Dai,Julie C Stout,Daniel J Upton,Jerome R Busemeyer,Rebecca Kerestes

doi:10.3389/fpsyg.2015.00229

Abstract

The Iowa Gambling Task (IGT) and the Soochow Gambling Task (SGT) are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning (EVL) model and the prospect valence learning (PVL) model, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79) and 27 control participants (mean age 35; SD 10.44) completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models.

Highlights

The Iowa Gambling Task (IGT; Bechara et al, 1994) and the Soochow Gambling Task (SGT; Chiu et al, 2008) are experience-based risky decision-making tasks
In this article we propose a new utility function and a new updating rule, which will be combined with complementary components in the expectancy-valance learning model (EVL) and prospect valence learning model (PVL) models to create new cognitive models of the IGT and SGT
Evidence that the cognitive models performed better than the baseline model came from the positive mean Bayesian information criterion (BIC) difference score of each cognitive model across both the IGT and SGT

Summary

Introduction

The Iowa Gambling Task (IGT; Bechara et al, 1994) and the Soochow Gambling Task (SGT; Chiu et al, 2008) are experience-based risky decision-making tasks. While IGT studies produced ambivalent results in terms of the relevant impacts of gain-loss frequency and expected value (e.g., Dunn et al, 2006), the choice pattern of healthy participants in the SGT suggested that gain-loss frequency is more influential than expected value in determining preference in such tasks. An important feature of the IGT and SGT is the complex interplay among motivational, cognitive, and response processes underlying the explicit choice behavior revealed in these tasks. Various cognitive models have been examined to disentangle this interplay of psychological processes underlying decision task performance, and successful ones are applied to clinical populations to identify reasons for disadvantageous choice patterns. Among them are the expectancy-valance learning model (EVL; Busemeyer and Stout, 2002) and the prospect valence learning model (PVL; Ahn et al, 2008), which have been successfully fitted to empirical data from a variety of healthy and clinical groups (Busemeyer and Stout, 2002; Stout et al, 2004; Yechiam et al, 2005; Lane et al, 2006; Fridberg et al, 2010)

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